added classification mettrics to info page

app.py

@@ -44,7 +44,7 @@ def load_interface():
 
     with info_page:
         # set title and description
-        gr.Markdown( #TODO: add information about our project: github, description, model metrics, etc.
+        gr.Markdown(
         """
         # Ensemble Classifier for Predicting Smoker or Non-Smoker
         
@@ -56,11 +56,30 @@ def load_interface():
         Our project focused on creating a classifier for a Kaggle dataset containing bio-signals and information on individuals' smoking status. The classifier aims to identify whether a patient is a smoker based on 22 provided features. You can find the dataset [here](https://www.kaggle.com/datasets/gauravduttakiit/smoker-status-prediction-using-biosignals?resource=download&select=train_dataset.csv). 
         We developed an Ensemble Classifier with Soft Voting, which combines KNN, SVM, and XGBoost classifiers.
 
+        - **non-smoker** = 0 
+        - **smoker** = 1
+
         ## Classifier Metrics
 
-        TODO: insert the classification report and confusion matrix
+        ### Classification Report
+
+        Train Accuracy:  0.7833977837414656
+        Test Accuracy:   0.7885084006669232
+
+                      precision    recall  f1-score   support
+
+          non-smoker       0.83      0.84      0.83      4933
+              smoker       0.72      0.69      0.71      2864
+
+            accuracy                           0.79      7797
+           macro avg       0.77      0.77      0.77      7797
+        weighted avg       0.79      0.79      0.79      7797
+
+        ## Confusion Matrix
+
+        ![](file/smoker_cm.png)
 
-        ## Report
+        ## Final Report
         For more details about our Ensemble Classifier and the individual models, please refer to our Jupyter notebooks in our project repository.
         [DSC 478 Project Repo](https://github.com/msoria17/dsc478-project)
         """